Framework for Implementing Segmented Dimensions

ABSTRACT

A system and method are disclosed for segmentation planning wherein a cost-to-serve interval and a value interval are used to generate a strategy for a micro-segment defined along a portion of the cost-to-serve interval and value interval, and similar micro-segments may be assigned to a single persona based on similar cost-to-serve and value tradeoffs.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/866,087, filed on Jul. 15, 2022, entitled “Framework for ImplementingSegmented Dimensions,” which is a continuation of U.S. patentapplication Ser. No. 14/216,477, filed on Mar. 17, 2014, entitled“Framework for Implementing Segmented Dimensions,” now U.S. Pat. No.11,397,957, which claims the benefit under 35 U.S.C. § 119(e) to U.S.Provisional Application No. 61/800,298, filed Mar. 15, 2013, andentitled “A Framework for Implementing Segmented Dimensions.” U.S.patent application Ser. No. 17/866,087, U.S. Pat. No. 11,397,957, andU.S. Provisional Application No. 61/800,298 are assigned to the assigneeof the present application.

TECHNICAL FIELD

The disclosure relates generally to a scalable and flexible framework ofplanning, sourcing, making, delivering, pricing, and sorting across aportfolio of businesses and specifically to the field of implementing aframework of segmented strategies and tactics.

BACKGROUND

Businesses often face an overwhelming amount of information aboutproducts and customers. A business owner may attempt to create productsto match the needs of certain customers. However, it is difficult toknow exactly what types of customers and products match well together.Further compounding this problem is how to target particular customersor products with various strategies and knowing the cost or value ofsuch strategies. This inability to flexibly target various customers andproducts while at the same time using data to automate routine planningand strategy updating based on data developed from implementing a planis undesirable.

SUMMARY

A system of segmentation planning is disclosed. The system includes acomputer system including a processor, a memory, a database includingvalues stored therein of one or more cost-to-serve intervals and one ormore value intervals associated with one or more entities, and asegmentation planner tangibly embodied on a non-transitory computerreadable medium. The segmentation planner defines a first dimension anda second dimension of the one or more entities, profiles the one or moreentities according to the one or more cost-to-serve intervals and theone or more value intervals and segments the first dimension and thesecond dimension for the one or more entities based on the one or morecost-to-serve intervals and the one or more value intervals. Thesegmentation planner also defines one or more micro-segments eachcomprising a segment of the first dimension and a segment of the seconddimension and assigns one or more strategies to the one or moremicro-segments based on the cost-to-serve and the value of entities inthe one or more micro-segments.

A method of segmentation planning is disclosed. The method includesreceiving values stored in a database of one or more cost-to-serveintervals and one or more value intervals associated with one or moreentities, defining a first dimension and a second dimension for the oneor more entities and profiling, by the computer, the one or moreentities according to the one or more cost-to-serve intervals and theone or more value intervals. The method further includes segmenting thefirst dimension and the second dimension for the one or more entitiesbased on the one or more cost-to-serve intervals and the one or morevalue intervals, defining one or more micro-segments each comprising asegment of the first dimension and a segment of the second dimension andassigning one or more strategies to the one or more micro-segments basedon the cost-to-serve and the value of entities in the one or moremicro-segments.

A non-transitory computer-readable medium embodied with software forsegmentation planning of one or more entities in a supply chain, each ofthe one or more entities associated with one or more cost-to-serveintervals and one or more value intervals, the software when executedusing one or more computers defines a first dimension and a seconddimension for the one or more entities. The software when executed usingone or more computers further profiles the one or more entitiesaccording to the one or more cost-to-serve intervals and the one or morevalue intervals and segments the first dimension and the seconddimension for the one or more entities based on the one or morecost-to-serve intervals and the one or more value intervals. Thesoftware when executed using one or more computers still further definesone or more micro-segments each comprising a segment of the firstdimension and a segment of the second dimension and assigns one or morestrategies to the one or more micro-segments based on the cost-to-serveand the value of entities in the one or more micro-segments.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be derived byreferring to the detailed description when considered in connection withthe following illustrative figures. In the figures, like referencenumbers refer to like elements or acts throughout the figures.

FIG. 1 illustrates an exemplary system according to an embodiment;

FIG. 2 illustrates a block diagram of segmentation planner according toan embodiment;

FIG. 3 illustrates a multi-dimensional cube according to an embodiment;

FIG. 4 illustrates the assignment of strategies to micro-segmentsaccording to an embodiment;

FIG. 5A illustrates an exemplary user interface according to anembodiment;

FIG. 5B illustrates an exemplary user interface according to anembodiment;

FIG. 5C illustrates an exemplary user interface according to anembodiment;

FIG. 5D illustrates an exemplary user interface according to anembodiment;

FIG. 5E illustrates an exemplary user interface according to anembodiment;

FIG. 5F illustrates an exemplary user interface according to anembodiment;

FIG. 5G (depicted as FIGS. 5G-1 and 5G-2 ) illustrates an exemplary userinterface according to an embodiment;

FIG. 5H (depicted as FIGS. 5H-1 and 5H-2 ) illustrates an exemplary userinterface according to an embodiment;

FIG. 5I (depicted as FIGS. 5I-1 and 5I-2 ) illustrates an exemplary userinterface according to an embodiment;

FIG. 5J illustrates an exemplary user interface according to anembodiment;

FIG. 6 illustrates a method for segmentation planning;

FIG. 7 (depicted as FIGS. 7A and 7B) illustrates customer value andcost-to-serve charts according to an embodiment;

FIG. 8 illustrates segmentation planner reports according to anembodiment;

FIG. 9 illustrates a user interface for variable segmentation accordingto an embodiment;

FIG. 10 illustrates adjustable parameter settings according to anembodiment;

and

FIG. 11 illustrates a block diagram of customer and product segmentationin a supply chain according to an embodiment.

DETAILED DESCRIPTION

Aspects and applications of the invention presented herein are describedbelow in the drawings and detailed description of the invention. Unlessspecifically noted, it is intended that the words and phrases in thespecification and the claims be given their plain, ordinary, andaccustomed meaning to those of ordinary skill in the applicable arts.

In the following description, and for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various aspects of the invention. It will beunderstood, however, by those skilled in the relevant arts, that thepresent invention may be practiced without these specific details. Inother instances, known structures and devices are shown or discussedmore generally in order to avoid obscuring the invention. In many cases,a description of the operation is sufficient to enable one to implementthe various forms of the invention, particularly when the operation isto be implemented in software. It should be noted that there are manydifferent and alternative configurations, devices and technologies towhich the disclosed inventions may be applied. The full scope of theinventions is not limited to the examples that are described below.

According to embodiments, this disclosure relates to systems and methodsthat involve a framework for implementing segmented planning.

FIG. 1 illustrates an exemplary system 100 according to a preferredembodiment. System 100 comprises segmentation planner 110, one or moreentities 120, computers 130, network 140, and communication links 142,144, and 146. Although a single segmentation planner 110, one or moreentities 120, a single computer 130, and a single network 140, are shownand described; embodiments contemplate any number of segmentationplanners 110, any number of entities 120, any number of computers 130,or any number of networks 140, according to particular needs.

System 100 may operate on one or more computers 130 that are integral toor separate from the hardware and/or software that support segmentationplanner 110 and one or more entities 120. Computers 130 may include anysuitable input device 132, such as a keypad, mouse, touch screen,microphone, or other device to input information. An output device 134may convey information associated with the operation of system 100,including digital or analog data, visual information, or audioinformation. Computers 130 may include fixed or removablecomputer-readable storage media, including a non-transitory computerreadable medium, magnetic computer disks, flash drives, CD-ROM,in-memory device or other suitable media to receive output from andprovide input to system 100. Computers 130 may include one or moreprocessors 136 and associated memory to execute instructions andmanipulate information according to the operation of system 100.

Although a single computer 130 is shown in FIG. 1 , segmentation planner110 and one or more entities 120 may each operate on separate computers130 or may operate on one or more shared computers 130. Each of the oneor more computers 130 may be a work station, personal computer (PC),network computer, notebook computer, tablet, personal digital assistant(PDA), cell phone, telephone, wireless data port, or any other suitablecomputing device. In an embodiment, one or more users may be associatedwith segmentation planner 110. These one or more users may include, forexample, a “planner” handling segmentation and/or one or more relatedtasks within system 100. In addition, or as an alternative, these one ormore users within system 100 may include, for example, one or morecomputers programmed to autonomously handle, among other things, settingparameters 470 a-n based on tactics 440 (see FIG. 4 ) and/or one or morerelated tasks within system 100. Additionally, “user” or “planner” mayrefer to a person performing this task interactively or a machine oralgorithm performing this task, with or without manual interaction.

In one embodiment, segmentation planner 110 is coupled with network 140using communications link 142, which may be any wireline, wireless, orother link suitable to support data communications between segmentationplanner 110 and network 140 during operation of system 100. One or moreentities 120 are coupled with network 140 using communications link 144,which may be any wireline, wireless, or other link suitable to supportdata communications between one or more entities 120 and network 140during operation of system 100. Computers 130 are coupled with network140 using communications link 146, which may be any wireline, wireless,or other link suitable to support data communications between computers130 and network 140 during operation of system 100.

According to one embodiment, entities 120 are internal or external to asupply chain. Typically, a supply chain receives supplies from one ormore suppliers and provides products to one or more customers. A supplychain may include any suitable number of nodes and any suitable numberof arcs between the nodes, configured in any suitable manner. The supplychain may have entities 120 such as customers, items, locations,channels, buyers, or any other entity. Items may comprise, for example,products, parts, or supplies that may be used to generate products. Anitem may comprise a part of the product, or an item may comprise asupply that is used to manufacture the product, but does not become apart of the product, for example, a tool, energy, or resource. The nodesof a supply chain may comprise, for example, locations where items areprocessed or where products are provided to a customer.

Although communication links 142, 144, and 146 are shown as generallycoupling segmentation planner 110, one or more entities 120, andcomputers 130 with network 140, segmentation planner 110, one or moreentities 120, and computers 130 may communicate directly withsegmentation planner 110, one or more entities 120, and/or computers130, according to particular needs.

In another embodiment, network 140 includes the Internet and anyappropriate local area networks (LANs), metropolitan area networks(MANs), or wide area networks (WANs) coupling segmentation planner 110,one or more entities 120, and computers 130. For example, data may bemaintained by segmentation planner 110 at one or more locations externalto segmentation planner 110 and one or more entities 120 and madeavailable to one or more associated users of one or more entities 120using network 140 or in any other appropriate manner. Those skilled inthe art will recognize that the complete structure and operation ofcommunication network 140 and other components within system 100 are notdepicted or described. Embodiments may be employed in conjunction withknown communications networks 140 and other components.

FIG. 2 illustrates a detailed view of an embodiment of segmentationplanner 110 coupled to transaction systems 230 and plan database 240. Asillustrated, segmentation planner comprises server 210 coupled bynetwork connection 220 to computer 130 comprising input device 132,output device 134 and one or more processors 136 and associated memoryto execute instructions and manipulate information according to theoperation of system 100. Although the depiction in FIG. 2 has computer130 internal to segmentation planner 110, as discussed above, computer130 may be externally coupled to segmentation planner 110 by anywireless or wireline connection.

According to an embodiment, server 210 comprises a plurality of modules:data profiler 211, segment and cluster module 212, strategy interface213, tactics database 214, plan exporter 215, rules 216 and clusteringalgorithms 217. Although various modules are shown and described on asingle server 210, each module or portion thereof may be present on oneor more servers 210 coupled by any wireless or wireline connection.

According to an embodiment, data profiler 211 receives entity data fromentities 120, computer 130, transaction systems 230, and/or plandatabase 240. A planner may modify entity data prior to sending entitydata to data profiler 211 or may edit the entity data directly therein.Data profiler 211 sorts and categorizes entity data as explained in moredetail in FIG. 3 . In addition, the operation of segment and clustermodule 212, strategy interface 213, tactics database 214, plan exporter215, rules 216 and clustering algorithms 217 will be explained in moredetail in FIG. 3 .

FIG. 3 illustrates a diagram of multi-dimensional cube 300 representingsegmentation of entity data. Multi-dimensional cube 300 comprisesdimensions 308, segments 310, micro-segments 312, andmicro-micro-segments 314, each comprising an x-axis 302, y-axis 304, andz-axis 306.

Each dimension of multi-dimensional cube 300 represents any quality,value, attribute, or characteristic useful in distinguishing or groupingentity data. By way of non-limiting example, dimensions may comprise oneor more of demographic attributes (such as age, education, gender,income, or the like), channel attributes, customer attributes, color,cost, geographical sector, lead time, price, product attributes, salesvolume, service level, variability, and value. Data profiler 211provides segmentation planner 110 to select one or more dimensions toprofile data, either by defining the dimension or by selecting from oneof a group of pre-selected dimensions. Furthermore, dimensions may becategorized according to which entity 120 or aspect of an entity 120 towhich the dimension relates. For example, embodiments contemplatecustomer dimensions, product dimensions, and channel dimensions. By wayof example only and not by limitation, customer dimensions may includeeducation, income, length of time as customer, gender, geographic sectoror region. Although, exemplary dimensions are shown and described,embodiments contemplate any dimensions according to particular needs.

After segmentation planner 110 defines or selects one or more dimensions308 of entity data using data profiler 211, data profiler 211 sorts theentity data corresponding to the defined or selected dimension 308according to customer value and/or cost-to-serve intervals. Eachinterval represents the entire range of values that the entity datatakes according to the various measurements of customer value and/orcost-to-serve in relation to the dimension 308 chosen.

For each interval, a segmentation planner 110 defines one or moreportions of the interval into segments 310 according to rules 216,clustering algorithms 217, or by segmentation analysis according toinput.

In one embodiment, data profiler 211 generates charts on output device134 that plot dimension 308 against one or more value or cost-to-servemeasurements, as illustrated in FIGS. 7-9 . As will be discussed morefully below, these charts permit a planner to define a segment, createrules 216 or clustering algorithms 217 to define a segment or determineif any value or cost-to-serve intervals cluster into one or moresegments 310 along dimension 308. Even if the intervals do not cluster,these charts serve as a graphical interface that permits segmentationplanner 110 to define a segment 310 of each dimension 308 by selectingcut-off values for each segment along a value or cost-to-serve interval.For example, where the dimension is “age of customer,” segmentationplanner 110 may select to define segments as “under 21,” “21-35,”35-55,” and “over 55,” based on the value or cost-to-serve of each agegroup. These dimensions 308 may likewise be subdivided into segments 310by a planner or by rules 216 and clustering algorithms 217 based thevalue or cost-to-serve, or on other criteria relevant to that dimensionas the following examples illustrate. For example, education may besubdivided into segments 310 i.e., none, basic, high school, anduniversity. Age may be subdivided into the segments 310 i.e., youth,young, mature, and aged. Gender may be subdivided into the segments 310i.e., male or female. Income may be subdivided into the segments 310i.e., low, middle, and high income. Geographic sector may be subdividedinto the segments 310 i.e., rural and urban. The foregoing dimensions308 and segments 310 are merely non-limiting examples, and specificexamples of dimensions 308 may be used in some embodiments as segments310 and vice-versa.

Furthermore, a segment 310 may be defined or expressed in any suitableformat, for example, numeric, string, or Boolean format. In addition, anumeric segment may be expressed as numeric value ranges such asabsolute value ranges. Relative segments may be expressed as relativevalues or as percentages of the entities. As an example only and not byway of limitation, for a volume dimension, a fast volume segment may bedefined as 0-80% of the volume, a medium segment may be defined as80-90% of the volume, and a slow segment may be defined as 90-100% ofthe volume. String segments may be expressed as a string represented bybuckets. Boolean segments may be expressed as a positive valueindicating that the Boolean segment is satisfied or a negative valueindicating that the Boolean segment is not satisfied.

Once each dimension 308 is selected and segments 310 for each dimension308 are selected or defined, the segment and cluster module 212organizes each segment-segment overlap into a micro-segment 312.

The segment and cluster module 212 receives dimension 308 and segment310 selections and data from data profiler 211 and receives input fromsegmentation planner 110 to organize the data into micro-segments 312according to rules 216 or clustering algorithms 217, which may be storedin the segment and cluster module 212, segmentation planner 110, and/ordeveloped by segmentation planner 110.

In a preferred embodiment, each micro-segment 312 comprises an overlapof segments 310 along each dimension 308. By way of non-limitingexample, the multi-dimensional cube 300 illustrated in FIG. 3 comprisesthree dimensions 308 each split into three segments 310. The number ofmicro-segments 312 is therefore twenty-seven. In a multi-dimensionalcube comprising two dimensions 308 with three segments 310 and twodimensions 308 with two segments 310, the number of micro-segments 312would be thirty-six. Although particular dimensions and segments andshown and described, embodiments contemplate any number of dimensions orsegments according to particular needs.

After segment and cluster module 212 sorts the entity data intomicro-segments 312, the segment and cluster module 212 groups themicro-segments into personas 320 based on rules 216 or clusteringalgorithms 217 stored therein. In some embodiments, a persona 320 is auser- or computer-defined grouping of micro-segments 312 that sharesimilar cost-to-serve and value tradeoffs so that a similar strategy 430(FIG. 4 ) may be used to target all micro-segments 205 in that persona320. For example, as shown in FIG. 3 first persona 320 a is illustratedas a cluster of four micro-segments 312 and second persona 320 b isillustrated as a cluster of three micro-segments 312.

In some embodiments, micro-segments 312 may be further segmented intomicro-micro-segments 314 according to particular needs. That is,micro-micro-segments 314 represent a micro-segment 312 that is segmentedaccording to further application of segments 310 along a dimension 308of a micro-segment 312.

In addition, although multi-dimensional cube 300 is shown and describedas a 3-dimensional cube in FIG. 3 , multi-dimensional cube 300 may alsobe represented by an n-dimensional shape comprising, for example, 1, 2,3, 4, . . . n dimensions 308. Likewise, each segment 310, micro-segment312, or micro-micro-segment 314 may comprise 1, 2, 3, 4, . . . nsegments 310, micro-segments 312, or micro-micro-segments 314,respectfully.

In addition, or as an alternative, all features of micro-segments 312may be applied to micro-micro-segments 314. Further discussion will onlyuse the phrase micro-segments 312, but should be understood asmicro-segments 312 and/or micro-micro-segments 314.

Continuing with FIG. 3 , after micro-segments 312 are defined andoptionally grouped into personas 320, segmentation planner 110 usesstrategy interface 213 to assign strategies 430 to one or more of themicro-segments 312 or personas 320 based on a combination of value andcost-to-serve considerations. Each strategy 430 in the strategyinterface 213 is associated with tactics 440, postures 450, and policies460, stored in tactics database 214. Strategy interface 213 may displaya graphical interface on computer 130 to a planner that allows selectionof a strategy 430 for each micro-segment 312 or persona 320 and allowsconfiguration of tactics 440, postures 450, and policies 460 accordingto particular needs. In some embodiments, tactics 440, postures 450, andpolicies 460 are pre-defined and stored in tactics database 208, wherespecific configurations of each of tactics 440, postures 450, andpolicies 460 are associated with each strategy 430.

Policies 460 represent supply chain policies that may be implementedinto a supply chain plan and executed across a supply chain. Planexporter 215 uses policies 460 to update plan data 242, constraints 244,and plan policies 246 in plan database 240. Policies 460 may also beused to update and configure transaction systems 230, according toparticular needs.

Plan exporter 215 also exports policies 460 to data profiler 211,segment and cluster module 212, and strategy interface 213 which monitortransaction systems 230 to provide data to segmentation planner 110 torefine segments 310, micro-segments 312, strategies 430, tactics 440,postures 450, and policies 460. Although an initial assumption may bemade, that a particular micro-segment 205 will behave a particular way,when a persona 320 is assigned, deviations will likely occur.Segmentation planner 110 provides the data received from transactionsystems 230 to update the configuration of tactics 440, postures 450,and policies 460 in the tactics database 214 to correct deviations inbehavior or performance, or the updating may be automatic based on rules216.

FIG. 4 illustrates the assignment of strategies 430 to variousmicro-segments 312 of multi-dimensional cube 300. Multi-dimensional cube300 of FIG. 4 comprises three dimensions, product 406, customer 408, andchannel 410, each split into three segments, represented by three rowsof blocks. In this example, product dimension 406 comprises threesegments 308. Customer dimension 408 comprises three segments 308, andchannel dimension 410 comprises three segments 308. Each intersection ofchannel dimension 410, customer dimension 408, and product dimension 406produces a single block, or micro-segment 312. The embodiment of themulti-dimensional cube 300 in FIG. 4 therefore comprises twenty-sevenblocks, or micro-segments 312. Each micro-segment 312 represents apotential target for strategy 430. In this manner, segmentation planner110 assigns strategy 430 to one or more particular micro-segments 312.In some embodiments, strategy 430 is assigned to micro-segment 412 inorder to grow a share of a micro-segment 432, retain share ofmicro-segment 414, retain profitability of micro-segment 416, or exitthe market for micro-micro-segment 418. Micro-segment 416 comprisesmicro-micro-segment 418, for exemplary purposes.

As discussed above, micro-segments 312 may be clustered together togroup similar behavior, or persona 320. In some embodiments, wheresegmentation planner comprises a user interface and the user interfacedisplays a multi-dimensional cube 300, micro-segments 312 sharing thesame persona 320 may be displayed with each color representing a singlepersona 320. By clustering multiple micro-segments 312 into the samepersona 320, segmentation planner 110 may target similar businessdecisions and/or strategies 430 to groups with, for example, similarvalue-cost trade-offs. In other words, the same value proposition may beoffered to multiple micro-segments 312 with common or substantiallysimilar cost-to-serve structures. In some embodiments, segmentationplanner 110 (e.g. segment and cluster module 212) forms micro-segments312 by grouping dimensions with a similar value proposition, or theparticular product, comprising particular features that a particularcustomer is buying at a particular price. In some embodiments, the valueproposition is juxtaposed with a cost-to-serve such that both intervalsor measurements may be considered in segmenting or assigning a strategy430. In one embodiment, multi-dimensional cube 300 identifiesmicro-segments that are particularly advantageous for a particularstrategy 430 by identifying each advantageous micro-segment 312 by aspecific color. For example, if segmentation planner 110 identifies amicro-segment 312 that shows high product features combined with lowcost, multi-dimensional cube 300 may indicate, by a color, that thissegment should be targeted with a grow share strategy (e.g.micro-segment 412 targeted with grow share strategy 432). Embodimentscontemplate any strategy or strategies 430, according to particularneeds.

Once strategy 430 has been assigned to a particular micro-segment 312,then segmentation planner 110 (e.g. strategy interface) displays a menuof tactics 440 or immediately assigns a pre-defined set of tactics 440to that micro-segment 312. Various strategies 430 may comprise differenttactics 440 as demonstrated by the following non-limiting examples. Fora grow share strategy 432, grow share tactics 442 are displayed orassigned by segmentation planner 110, which may include: highavailability, aggressive delivery lead times, broad assortment, and/oraggressive dynamic pricing. For a retain share strategy 434, retainshare tactics 444 are displayed or assigned by segmentation planner 110,which may include: fixed price, competitive delivery lead times, and/orallocated availability. For a retain profitability strategy 436, retainprofitability tactics 446 are displayed or assigned by segmentationplanner 110, which may include: cost-plus pricing, modest availability,and/or modest delivery lead times. For an exit strategy 438, exittactics 448 are displayed or assigned by segmentation planner 110, whichmay include: minimize cost, honor existing commitments only, and/or nonew sales. As an example only and not by way of limitation, if thestrategy is grow share 432, grow share tactics 442 are displayed orassigned by segmentation planner 110, which may comprise variouscombinations of tactics 440 that will accomplish this strategy.Segmentation planner 110 may analyze data received from plan database240, transaction systems 230, or elsewhere in system 100 to determinefactors that affect which type of various tactics 442 a grow sharestrategy 432 will employ, such as the fastest, most efficient, orcheapest tactics 440 for achieving a grow share strategy 432.Embodiments contemplate other tactics 440 to achieve any strategy 430.For each tactic 440, segmentation planner 110 displays a template ofsuggested postures 450. In addition, or as an alternative, segmentationplanner 110 assigns a posture 450 to micro-segment 312 immediately uponselection of a particular tactic 440.

Once strategies 430 and tactics 440 are assigned, segmentation planner110 assigns parameters 470 to implement tactics 440 for eachmicro-segment 412. Each specific configuration of parameters 470 may betermed a posture 450. For example, grow share strategy 432 may have agrow share posture 452. Retain share strategy 434 may have a retainshare posture 454. Retain profitability strategy 436 may have a retainprofitability posture 456. Likewise, exit strategy 438 may have an exitstrategy posture 458.

Segmentation planner 110 may modify a posture 450 by changing individualparameters 470, according to particular needs. The number of parameters470 displayed by segmentation planner 110 may be customized according toparticular needs. For example, although FIG. 4 illustrates six growshare parameters 470 a-f, embodiments contemplate any number ofparameters 470 a-n, according to particular needs.

In some embodiments, grow share posture 452 may comprise grow shareparameters 470 a-n. Parameters 470 may be configured using slider barsdisplayed in user interface and which indicate a relative positionranging from “Agile” (or “A”) on one extreme, “Efficient” (or “E”) onthe other extreme, and “Balanced” (or “B”) in the middle, such thatdifferent parameters 470 may be assigned specific values depending onthe particular posture 450 selected. These parameters 470 are stored intactics database 214 and may be modified by segmentation planner 110according to rules 216 or clustering algorithms 217 stored therein.Parameters 470 of postures 450 are user-configurable depending on aparticular micro-segment 312. Examples of parameters 470 that may beconfigured are: plan 470 a, source 470 b, make 470 c, deliver 470 d,price 470 e, assort 470 f, and any combination of these or additionalparameters 470. Same, similar or further parameters 470 are assignableto any strategy 430, according to particular needs.

For example, plan parameter 470 a is user-adjustable between anefficient setting, an agile setting, and a balanced setting. Uponselection of an efficient setting of plan parameter 470 a, segmentationplanner 110 may update plan data 242, constraints 244, plan policies246, and/or transaction systems 230 to cause a supply chain plan to beupdated monthly and for the updating to be automated, based, forexample, on general assumptions about demand. An agile setting of planparameter 470 a may update plan data 242, constraints 244, plan policies246, and/or transaction systems 230 to cause supply chain plan toupdated daily based on forecast and demand. A balanced setting of planparameter 470 a may update plan data 242, constraints 244, plan policies246, and/or transaction systems 230 to cause supply chain plan to beupdated on some time period between a month and a day and for the planto be based on some combination of forecast, demand, and assumptions.Although particular time intervals are described associated with anefficient, agile and balanced settings, a supply chain plan may beupdated at any time interval, according to particular needs.

Similarly, user-selection of an agile or efficient setting of aparameter (a balanced setting being in the middle or a combination ofthe agile and efficient policy) causes updates to plan data 242,constraints 244, policies 245, and/or transaction systems 230 asillustrated in the following table:

TABLE 1 Parameter Agile Efficient Source Multi-source Bulk Buy Flexiblecontracts Economic Order Quantity COS Buffer Stocks Strategic buy LockedVolume Make Assemble to Order Build to Stock Make to Order Max CapacityCapable to Promise Low Changeovers High Touch High Changeovers DeliverExpedites No expedites Air Ocean Less than Truck Load Full Truck LoadFlexible Capacity Economic Order Quantity

In one embodiment, other parameters 470 are useful for supply chainplanning purposes, and policies 245 affected by these parameters 470include: service levels, postponement models, offered lead times,priorities for tiered budget, demand segment attributes, product segmentattributes, supply and/or capacity segment attributes, forecast horizon,forecast lag, model selection, distribution requirements plan/masterplanning schedule coverage duration, safety stock coverage, safety stockrule, replenishment quantities, ship quantities, forecast adjustment,proration, consumption, master planning priorities, order promising,promising polices, and/or allocation policies. One having skill in theart would recognize how to implement these parameters according to someembodiments based on this disclosure. Posture 450 may be formed from oneor more of any combination of these or other parameters 470.

In some embodiments, each strategy 430 or tactic 440 is assignable toposture 450 stored in tactics database 214 that has been pre-determinedto achieve that strategy 430. In some embodiments, parameters 470 areadjusted by segmentation planner 110 for each planning cycle based ondata received from plan database 240 or transaction systems 230 in orderto better correlate a posture 450 to achieve a specific strategy 430.

After posture 450 has been selected by segmentation planner 110 andparameters 470 are defined, segmentation planner 110 assigns policies460 as described above to plan data 242, constraints 244, plan policies246, and/or transaction systems 230 in order to implement strategy 430on which posture 450 is based. According to some embodiments,segmentation planner 110 prepares policies 460 based on the strategy430, tactics, 440, and/or postures 450. For example, selection of a growshare strategy 432 may set a grow share policy 462 to all entities 120comprising micro-segment 412, taking into consideration anymodifications to parameters 452 made by segmentation planner 110.Similarly, a retain share tactic 434 may correlate to a retain sharepolicy 464; a retain profitability tactic 436 may correlate to a retainprofitability policy 466; and an exit tactic 438 may correlate to anexit policy 468. As an example only and not by way of limitation, a growshare policy 462 may comprise, for example, a days of coverage (DOC) of14, service level equal to 99.9%, forecast lag of weekly, forecastprocess of stat (i.e. statistical forecasting process), and assortmentequal to broad (i.e. a broad assortment of options for a given entity).In some embodiments, once postures 450 are set using, for example, theslider bars for parameters 470, all policies 460 associated with asupply chain are immediately implemented, as discussed above.Embodiments contemplate any policies 460, according to particular needs.

To further illustrate segmentation performed by segmentation planner110, a non-limiting example is now given. In the following example,FIGS. 5A-5J illustrate an exemplary scenario of segmenting an exemplaryportfolio of entities 120 using an exemplary user interface 500. In someembodiments, exemplary user interface 500, comprises one or more modulesrepresented by graphical elements such as portfolio panel 501 anddimension panel 502. Portfolio panel 501 comprises one or more inputboxes including a portfolio name input box 510, portfolio descriptioninput box 511 and portfolio members input box 512. A user may input theappropriate information in the portfolio input boxes 510-512 accordingto the desired name, description, and number of members of theportfolio. In the exemplary scenario, the portfolio name 510 is “ChileanPopulation,” the portfolio description 511 is “Chilean Population,” andthe total members 512 are 17,000,000. In some embodiments, each of theinput boxes 510-512 is auto-populated upon selection of portfolio bysegmentation planner 110.

In some embodiments, exemplary user interface 500 comprises one or moredimension panels 502. Each dimension panel 502 represents a separatedimension 308 to analyze for a particular portfolio. Each dimension 308may be further subdivided into segments 310. In the illustratedembodiment, the Chilean Population portfolio is segmented into fivedimensions 308, each having segments 310. In illustrated embodiment, agedimension 503 comprises four segments 310: youth, young, mature andaged; income dimension 504 comprises three segments 310: low, middle andhigh income; gender dimension 505 comprises two segments 310: male andfemale; education dimension 506 comprises four segments 310: uneducated,elementary education, high school diploma and college educated; andgeographic sector dimension 507 comprises two segments 310: rural andurban. Although a particular number of dimensions 308 and segments 310are shown and described, embodiments contemplate any number ofdimensions comprising any number of segments, according to particularneeds.

In addition, each dimension panel 502 comprises dimension name input box513, portfolio drop down box 514, and chart 515, which may compriseheadings 516 for segment, description, and percent.

FIG. 5B illustrates further features of exemplary user interface 500. Asillustrated, user interface 500 comprises multi-select helper panel 520,micro-segment grid panel 521, and micro-segment pie chart 522.Additional features include segmentation dimensions panel 523, rowlabels 526, and column labels 527. In some embodiments, segmentationdimensions 523 are dragged 524 into micro-segment grid chart 521, rowlabels 526, and column labels 527 to update the micro-segment grid chartby populating the segmentation dimensions 523 as the column or row towhere it is dragged. When a first dimension is selected for a column anda second dimension is selected for a row, a micro-segment grid panel 521populates the segments 310 of each dimension 308 along the row andcolumn. The micro-segment grid panel then populates the number ofmembers of the portfolio that belong to each intersection of eachsegment. Each intersection of segments 310 is a micro-segment 312. Forexample, in FIG. 5B, column dimension 308 is selected as income and rowdimension 308 is selected as education. In response to the selection,micro-segment grid chart 521 populates income segments along the columnof the micro-segment grid chart 521. The micro-segment grid chartpopulates the education segments along the row of the micro-segment gridchart 521. The micro-segment grid chart calculates the number of memberswho belong to the micro-segment represented by each intersection of rowand column. For example, at the intersection of low income and noeducation, the micro-segment grid chart 521 automatically populates the714,000 members of the Chilean Population portfolio described in FIG. 5Athat are both low income and have no education. Totals of each column,row, or both may also be depicted in the micro-grid segment chart 521.As an example only and not by way of limitation, a weighted distributionis provided that shows that there are 714,000 people that have lowincome and no education, and this is calculated for each of themicro-segment grids. In this exemplary scenario, this adds up to thetotal population of 17 million.

FIG. 5C illustrates an exemplary embodiment of assigning group labels tomicro-segments. In this exemplary scenario, one or more micro-segmentsare grouped together into a persona under a single group label. When anassign group action 528 is initiated, by, for example, selecting a menuoption or right clicking on a micro-segment in the micro-segment gridchart, a dialog box 529 appears including group name drop-down box 530and description input box 531. A group name may be selected from thegroup name drop-down box 530, which will cause segmentation planner 110to assign a group label and a color to the selected micro-segment. Auser may specify in the description input box 531 text that will beuseful in identifying the group that has been assigned. In someembodiments, a segmentation planner will automatically assign a distinctcolor for each new group. In other embodiments, a user may select fromexisting groups in the group label drop-down box 530 or create a newgroup label by typing in a new name. In the illustrated embodiment,“Untargeted” is selected as the group label for the micro-segmentcomprising the Chilean Population with low income and no education. InFIG. 5D, the micro-segment comprising the Chilean Population with lowincome and no education has been assigned a color, and a Groups label532 has been created that lists the names of groups that have beenassigned in the micro-segment grid chart 521. Furthermore, pie chart 522is automatically populated depicting the untargeted group 533 and thenon-categorized group 534.

FIG. 5E illustrates use of multi-select helper panel 520 to assign grouplabels to micro-segments. Multi-select help panel 520 comprises columnheadings representing one or more dimensions. Rows under each column mayrepresent one or more segments. Each column heading dimension and eachrow segment may be associated with a radio selection box. If a radioselection box for a column heading is selected, each row in that columnis automatically selected. Additionally, a radio selection box may beselected next to a row segment. Each row segment that is highlighted mayappear as a selection of micro-segments in the micro-segment grid chart521 that fulfill all the intersection of each row segment selectionselected in the multi-select helper panel 520. In some embodiments, ifall segments of a dimension are selected, that dimension is notrepresented in the micro-segment grid chart 521. In the illustratedembodiment, a group label, “reluctant pragmatists” is associated withthe age segments youth and young, the gender segments male and female,the education segments, basic, high school, and university, the incomesegment, middle income, and the geographical sector rural and urban. Inthe illustrated embodiment, the dimensions of gender and geographicsector are not represented on micro-segment grid chart 521. Thereluctant pragmatists label color is automatically assigned to themicro-segments that fulfill all the requirements that are selected inthe multi-select helper panel 520. Pie chart 522 is automaticallyupdated with the number of reluctant pragmatists depicted on the chart535.

FIG. 5F illustrates using a multi-select helper panel 520 to select anadditional micro-segment to assign to the group reluctant pragmatists.In the illustrated embodiment, the age segment, mature, gender segmentsmale and female, the education segment, university, the income segment,middle income, and the geographical sectors, rural and urban have beenselected. The group label reluctant pragmatists is then assigned to theone or more micro-segments that belong to these segments. Themicro-segment is assigned the same color as the other selected reluctantpragmatists micro-segments and the pic chart 522 is updated accordingly.

FIG. 5G (depicted as FIGS. 5G-1 and 5G-2 ) illustrates an exemplaryembodiment displaying a user interface with the segments of geographicsector, income and age dimension with the gender and education dimensiondisplayed. Group labels have been assigned to untargeted and reluctantpragmatists. Segmentation dimensions 523 displays all segments depictedon the micro-segment grid chart 521. Row labels 526 display the rowsdisplayed on the micro-segment grid chart; and column labels 527 displaythe columns displayed on the micro-segment grid chart.

FIG. 5H (depicted as FIGS. 5H-1 and 5H-2 ) illustrates the further grouplabel endearing fan added to the micro-segment grid chart 521. Thegroups 532 is automatically updated with the group label, and theselected micro-segments are automatically colored according to the colordepicted next to the group label. The pie chart 522 is updated withnumbers representing the amount of members contained in themicro-segments assigned to each of the groups. FIG. 5I (depicted asFIGS. 5I-1 and 5I-2 ) is similar and illustrates the further grouplabel, selfish elitists, updated in the user interface.

FIG. 5J illustrates a summary of the micro-segment assignments accordingto the exemplary scenario. As discussed above, although particulardimensions 308, segments 310, micro-segments 312 and personas 320 areshown and described, embodiments contemplate any particular dimensions308, segments 310, micro-segments 312 or personas 320, according toparticular needs.

FIG. 6 illustrates a flow chart of an exemplary method representing a“bottom-up” embodiment of segmentation. The method begins at step 602,where segmentation planner 110 identifies one or more entities 120 ofthe supply chain. As discussed above, an entity may be, for example,products, customers, channels, locations and the like. Embodimentscontemplate any number of entities, according to particular needs. Next,in step 604, segmentation planner 110 defines one or more dimensions 308for the one or more entities 120. As an example only and not by way oflimitation, these dimensions may include price, lead time, servicelevel, number of features, colors, forecast error, variability, andvolume. Embodiments contemplate any number of dimensions, according toparticular needs.

In step, 606, segmentation planner 110 profiles the dimension accordingto customer value and cost-to-server intervals, by for example, miningthe entity data using histograms and Pareto charts, curves and the likeand generating curves and boundaries, as illustrated in FIG. 7 (depictedas FIGS. 7A and 7B).

In step 608, segmentation planner 110 defines the segments 310 alongeach dimension 308. In some embodiments, each dimension is segmentedinto, for example, a first, second, and third segment 310, one dimension308 at a time, as illustrated in FIG. 7 (depicted as FIGS. 7A and 7B).At step 610, a determination is made whether to define one or moreadditional dimensions 308. If further dimensions 308 are to be defined,the method returns to step 604 to define the next dimension 308. If afurther dimensions 308 are not to be defined, the method proceeds tostep 612.

In step 612, segmentation planner 110 defines micro-segments 312 bycombining segments 310 using rules 216 or clustering algorithms 217. Asan example only and not by limitation, micro-segment 312 may be thecombination of high volume segment 308, low variability segment 308, andhigh margin segment 308. In some embodiments, segmentation planner 110employs clustering algorithms 217 and/or rules 216 to utilize entitydata and develop segments 308 or micro-segments 312 that demonstrate,for example, clusters or ranges of data that identify micro-segments.These clusters or ranges of data may be represented by histograms or bymulti-dimensional cube 300.

In step 614, segmentation planner 110 assigns a strategy 430 tomicro-segment 312. As discussed above, available strategies 430 includegrow share, retain share, retain profitability, exit, or the like.Embodiments contemplate any strategy or any number of strategiesaccording to particular needs. At step 616, a determination is madewhether to assign strategies 430 to additional micro-segments 312. Ifadditional strategies 430 are to be assigned to other micro-segments312, the method returns to step 612 to define the next micro-segment312. If additional strategies 430 are not to be assigned to moremicro-segments 312, the method proceeds to step 618.

In step 618, segmentation planner 110 associates tactics 440 to eachstrategy 430 and micro-segment 312 with associated targets for keymetrics. In some embodiments, tactics 440 include high availability,aggressive delivery lead times, broad assortment, aggressive dynamicpricing and the like. Embodiments contemplate any tactic, according toparticular needs.

In step 620, segmentation planner 110 assigns parameters 470 to eachtactic 440, strategy 430, or micro-segment 312 to implement the tactics440 associated with each micro-segment in step 618. Next, in step 622,segmentation planner 110 calculates and displays the cost-to-serveand/or value interval for each micro-segment 312 for comparativeanalysis. In step 624, segmentation planner 110 updates plan data 242,constraints 244, and/or plan policies 246 and executes the plan. At step626, segmentation planner 110 assigns Key Performance Indicators (KPIs)to each micro-segment 312 and monitors performance of each micro-segmentaccording to the KPIs. Segmentation planner 110 generates refined dataand values for micro-segments 312, strategies 430, tactics 440, andparameter 470 settings. The method comprises a learning feedback loop atstep 628, which recognizes and reacts to unplanned events and changingconditions and refines micro-segments 312, strategies 430, tactics 440,and parameter 470 settings.

FIG. 7 (depicted as FIGS. 7A and 7B) illustrates cost-to-serve andcustomer value charts. In some embodiments, segmentation planner 110generates one or more customer value charts 700 illustrating, forexample, average realized price 710, 80th percentile of requested leadtimes 720, or required service levels 730. In some embodiments, wherevalue equates to price, service, urgency, or some combination of likeconcepts, value may also be more or fewer dimensions. In addition tovalue charts 700, segmentation planner 110 generates one or morecost-to-serve charts 705, which illustrate, for example, realized cost740, historical volume 750, forecast accuracy 760, or demand variability770. Cost-to-serve chart 705 illustrates the cost to deliver a value. Insome embodiments, segmentation planner 110 generates a Pareto chart,illustrated by lines 741 and 751, in realized cost chart 740 andhistorical volume chart 750, respectively. In some embodiments,cost-to-serve comprises one or more dimensions including, for example,product, customer, channel, or a combination of one or more of likeconcepts, e.g., a specific product for a specific customer.

Segmentation of a portfolio along cost-to-serve and value intervalsprovides a method to design supply chain cost structures that matchappropriate customer value, for example, matching a low cost structurewith a low customer value expectation. In this way, a supply chain coststructure can be designed which avoids high cost structures supporting alow customer value expectation. Embodiments contemplate value andcost-to-serve calculated according to any appropriate value and/orcost-to-serve rule or formula.

FIG. 8 illustrates for the sake of simplicity and without loss ofgeneralization, embodiments representing exemplary reports generated bysegmentation planner 110. Embodiments contemplate any number of reports,according to particular needs. As an example only and not by way oflimitation, these reports may include bubble chart of Market Growthversus Market Share 801, further broken down by revenue and margin, abubble chart of Revenue per Quarter versus Length of Time as Customer802, further broken down by demand variability, and various chartsrepresenting revenue performance 803, offered lead times 804, fill rate805, and on time delivery 806.

FIG. 9 illustrates an exemplary user interface for adjusting ofsegmentation. Segmentation planner 110 generates bubble chart 900depicting retail customers, contribution, growth and revenue anddisplays on output device 134. Bubble chart 900 comprises a Y-axisslider 910 and an X-axis slider 915. A user may slide a pointer on eachof these sliders to subdivide or segment various dimensions according touser preference. For example, a user may slide the Y-axis slider 910representing a 3-year compound annual growth rate (CAGR) percentage tomove a Y-axis divider line 911 according to a user preference.Similarly, a user may slide the X-axis slider 915 representing revenuecontribution to move an X-axis divider line 916 according to a userpreference. In this manner a user defines segments 310 and/ormicro-segments 312. In some embodiments, a user classification may causesegmentation planner 110 to update a database with a user-specifiedclassification 905. For example, a Y-axis divider line 911 may dividecustomers into low-growth and high-growth customers based on a 3-yearCAGR. A user may adjust the Y-axis divider line to place some customersinto a high-growth category and others into a low growth category.Similarly, an X-axis divider line 916 may divide customers intolow-contribution and high-contribution customers based on a revenuecontribution percentage. A user may adjust the X-axis divider line toplace some customers into a low-contribution category and others into ahigh-contribution category.

Demand variability chart 920 illustrates use of a two-pointer verticalslider 940. Demand variability is illustrated by bar chart 920. Anynumber of entities, here customers, may be illustrated by the chart. Auser may adjust a first slider 935 to move a first vertical divider line936 to divide customers between a first and second segment 310. A usermay adjust a second slider 930 to move a second vertical divider line931 to divide customers between a second and third segment 310. In thismanner, various entities can be categorized into various segments 310 bya user interface. Embodiments contemplate any number of entities 120,segments 310, or sliders according to various needs.

FIG. 10 illustrates using parameters 470 for particular historicaldemand variability, particular historical margin, and particular volume.FIG. 10 is an exemplary embodiment demonstrating that differentparameters 470 may be used to effect different strategies 430 fordifferent situations. In some embodiments, a user may adjust variousparameters 470 based on various needs. Embodiments contemplate anynumber or combination of parameters 470 for any number or combination ofdimensions 308 or segments 310 to be planned with segmentation planner110.

FIG. 11 illustrates a block diagram of customer and product segmentationintegration into a supply chain system 1100. As discussed above, asupply chain may have one or more entities 120 such as customers, items,locations, channels, buyers. Each block 1101-1108 represents one or morecomputer systems or modules which are programmed to effectuate thelisted operation. Any of the systems or modules can be operated on thesame or different computers, according to particular needs. Segmentationplanner 110 modifies rules, policies, constraints, and other types ofdata on the systems illustrated in FIG. 11 according to the followingdisclosure.

Order creation in supply chain system 1100 begins with EnterpriseResource Planner 1108, which sends and receives queries and transactions1121 from order promising system 1107. Order promising system 1107comprises policies for allocating orders among customers. Orderpromising system 1107 receives order promises 1120 from allocatedAvailable-to-Promise (ATP) system 1106 and sends queries andtransactions 1119 to allocated ATP system 1106. Order promising system1107 sends committed orders 1117 to forecast netting system 1104 andsupply chain master planning system 1105. Segmentation planner 110, inresponse to a user selection of a strategy 430, modifies the policies oforder promising system 1107 to effectuate the selected strategy 430.

Forecast netting system 1104 receives forecasts 1113 from demandplanning system 1102 and sends netted forecasts 1115 to supply chainmaster planning system 1105. Segmentation planner 110, in response to aselection of strategy 430, modifies profiling policies and nettingpolicies of forecast netting system 1104 to effectuate the selectedstrategy 430.

Demand planning system 1102 sends forecasts 1113 to inventory planningsystem 1103, sends unconstrained forecasts 1109 to sales and operationsplanning system 1101, and receives demand review 1110 from sales andoperations planning system 1101. Segmentation planner 110, in responseto a selection of strategy 430, modifies a demand planning configurationof the demand planning system 1102 to effectuate the selected strategy430.

Inventory planning system 1103 sends fulfillment and safety stockpolicies 1116 to supply chain master planning system 1105. Segmentationplanner 110, in response to a selection of strategy 430, modifiesservice levels, offered lead times, and/or reorder policies of inventoryplanning system 1103 to effectuate the selected strategy 430.

Sales and operations planning system 1101 receives supply commit toforecast and orders 1111 from supply chain master planning system 1105and sends supply review 1112 to supply chain master planning system1105. Segmentation planner 110, in response to a selection of a strategy430, modifies customer segmentation and product segmentation of salesand operation planning system 1101 to effectuate the selected strategy430.

Supply chain master planning system 1105 sends ATP data 1118 toallocated ATP system 1106. Segmentation planner 110, in response to aselection of strategy 430, modifies demand prioritization and safetystock ranks and/or bands of supply chain master planning system 1105 toeffectuate the selected strategy 430. Additionally, segmentation planner110, in response to a selection of strategy 430, modifies allocationpolicies of allocated ATP system 1106 to effectuate the selectedstrategy 430.

Reference in the foregoing specification to “one embodiment”, “anembodiment”, or “another embodiment” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the invention. The appearancesof the phrase “in one embodiment” in various places in the specificationare not necessarily all referring to the same embodiment.

While the exemplary embodiments have been shown and described, it willbe understood that various changes and modifications to the foregoingembodiments may become apparent to those skilled in the art withoutdeparting from the spirit and scope of the present invention.

What is claimed is:
 1. A system of segmentation planning, comprising: a computer system comprising a processor, a memory, and a database, the computer system: receives data from one or more entities by a network connected with supply chain transaction systems; profiles the one or more entities according to one or more cost-to-serve intervals and one or more value intervals; defines one or more micro-segments of a multi-dimensional cube such that each micro-segment comprises an overlap of a segment of a first dimension and a segment of a second dimension; assigns one or more strategies to the one or more micro-segments based on a cost-to-serve interval and a value interval of entities associated with the one or more micro-segments; implements predefined tactics to achieve the one or more strategies assigned to the one or more micro-segments by updating the supply chain transaction systems; monitors, autonomously without human intervention, data from the supply chain transaction systems indicating unplanned events and changed conditions and in response to the monitoring, a learning feedback loop refines the one or more micro-segments and updates the predefined tactics to achieve the one or more strategies; and adjusts the implementing of the predefined tactics by further updating the supply chain transaction systems.
 2. The system of claim 1, wherein the supply chain transaction systems include warehouse management systems, manufacturing execution systems, transportation management systems and enterprise resource planning systems or a combination thereof.
 3. The system of claim 1, wherein the computer system further: associates the one or more strategies with predefined tactics; and associates the predefined tactics with predefined postures, wherein each of the predefined postures are associated with a policy that represents a supply chain policy implemented into a supply chain plan, the database further comprising values stored therein of one or more cost-to-serve intervals and one or more value intervals associated with the one or more entities.
 4. The system of claim 1, wherein the computer system further: creates the multi-dimensional cube by segmenting the first dimension and the second dimension for the one or more entities based on the one or more cost-to-serve intervals, the one or more value intervals and the one or more rules.
 5. The system of claim 1, wherein the computer system further: provides a graphic user interface for subdividing or segmenting one or more of the first dimension and the second dimension.
 6. The system of claim 1, wherein the computer system further: mines data of the one or more entities to profile the first dimension and the second dimension using one or more histograms.
 7. The system of claim 1, wherein the multi-dimensional cube further comprises an n-dimensional shape.
 8. A method of segmentation planning, comprising: receiving, by a computer, data from one or more entities by a network connected with supply chain transaction systems, wherein the received data is stored in a database; profiling, by the computer, the one or more entities according to one or more cost-to-serve intervals and one or more value intervals; defining, by the computer, one or more micro-segments of s multi-dimensional cube such that each micro-segment comprising an overlap of a segment of a first dimension and a segment of a second dimension; assigning, by the computer, one or more strategies to the one or more micro-segments based on a cost-to-serve interval and a value interval of entities associated with the one or more micro-segments; implementing, by the computer, predefined tactics to achieve the one or more strategies assigned to the one or more micro-segments by updating the supply chain transaction systems; monitoring, autonomously without human intervention, data from the supply chain transaction systems indicating unplanned events and changed conditions and in response to the monitoring, a learning feedback loop refines the one or more micro-segments and updates the predefined tactics to achieve the one or more strategies; and autonomously adjusts the implementing of the predefined tactics by further updating the supply chain transaction systems.
 9. The method of claim 8, wherein the supply chain transaction systems include warehouse management systems, manufacturing execution systems, transportation management systems and enterprise resource planning systems or a combination thereof.
 10. The method of claim 8, further comprising: associating, by the computer, the one or more strategies with predefined tactics; and associating, by the computer, the predefined tactics with predefined postures, wherein each of the predefined postures are associated with a policy that represents a supply chain policy implemented into a supply chain plan, the database further comprising values stored therein of one or more cost-to-serve intervals and one or more value intervals associated with the one or more entities.
 11. The method of claim 8, further comprising: creating, by the computer, the multi-dimensional cube by segmenting the first dimension and the second dimension for the one or more entities based on the one or more cost-to-serve intervals, the one or more value intervals and the one or more rules.
 12. The method of claim 8, further comprising: providing, by the computer, a graphic user interface for subdividing or segmenting one or more of the first dimension and the second dimension.
 13. The method of claim 8, further comprising: mining, by the computer, data of the one or more entities to profile the first dimension and the second dimension using one or more histograms.
 14. The method of claim 8, wherein the multi-dimensional cube further comprises an n-dimensional shape.
 15. A non-transitory computer-readable medium embodied with software for segmentation planning, the software when executed using one or more computers: receives data from one or more entities by a network connected with supply chain transaction systems; profiles the one or more entities according to one or more cost-to-serve intervals and one or more value intervals; defines one or more micro-segments of a multi-dimensional cube such that each micro-segment comprising an overlap of a segment of the first dimension and a segment of the second dimension; assigns one or more strategies to the one or more micro-segments based on a cost-to-serve interval and a value interval of entities associated with the one or more micro-segments; implements predefined tactics to achieve the one or more strategies assigned to the one or more micro-segments by updating the supply chain transaction systems; monitors, autonomously without human intervention, data from the supply chain transaction systems indicating unplanned events and changed conditions and in response to the monitoring, a learning feedback loop refines the one or more micro-segments and updates the predefined tactics to achieve the one or more strategies; and autonomously adjusts the implementing of the predefined tactics by further updating the supply chain transaction systems.
 16. The non-transitory computer readable medium of claim 15, wherein the supply chain transaction systems include warehouse management systems, manufacturing execution systems, transportation management systems and enterprise resource planning systems or a combination thereof.
 17. The non-transitory computer readable medium of claim 15, wherein the software when executed by one or more computers further: associates the one or more strategies with predefined tactics; and associates the predefined tactics with predefined postures, wherein each of the predefined postures are associated with a policy that represents a supply chain policy implemented into a supply chain plan, the database further comprising values stored therein of one or more cost-to-serve intervals and one or more value intervals associated with the one or more entities.
 18. The non-transitory computer readable medium of claim 15, wherein the software when executed by one or more computers further: creates the multi-dimensional cube by segmenting the first dimension and the second dimension for the one or more entities based on the one or more cost-to-serve intervals, the one or more value intervals and the one or more rules.
 19. The non-transitory computer readable medium of claim 15, wherein the software when executed by one or more computers further: provides a graphic user interface for subdividing or segmenting one or more of the first dimension and the second dimension.
 20. The non-transitory computer readable medium of claim 18, wherein the software when executed by one or more computers further: mines data of the one or more entities to profile the first dimension and the second dimension using one or more histograms. 